Evidence map›Paper›PMID 33374208›Full record

ArticleSensors (Basel, Switzerland)2020

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards-Case Study: Workplace Reopening.

Steve H L Liang, Sara Saeedi, Soroush Ojagh, Sepehr Honarparvar, Sina Kiaei, Mahnoush Mohammadi Jahromi, Jeremy Squires

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. COVICT: an IoT based architecture for COVID-19 detection and contact tracing.Journal of ambient intelligence and humanized computing · 2023
    Article
  5. Article
  6. IoMT-fog-cloud based architecture for Covid-19 detection.Biomedical signal processing and control · 2022
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Steve H L LiangDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.
Sara SaeediDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.ORCID 0000-0003-1480-6382
Soroush OjaghDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.ORCID 0000-0003-4773-1938
Sepehr HonarparvarDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.
Sina KiaeiDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.
Mahnoush Mohammadi JahromiDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N1N4, Canada.
Jeremy SquiresSensorUp Inc., Calgary, AB T2L2K7, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

Indexed as

COVID-19Environmental Restoration and RemediationHealth Risk BehaviorsHumansInternet of ThingsPandemicsReference StandardsSoftwareWorkplacecontact tracingcough detectionCOVID-19deep learningInternet of Things (IoT), interoperabilityOGC IndoorGMLOGC SensorThingsphysical distance detectionspatial multi-criteria risk analysis

Identifiers

PMID33374208
PMCPMC7796058

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.